The reinterpretation of the whole communication chain as an autoencoder-based system [1]} pioneered a number of related works [2]}, [3]}, [4]} aimed at showing the potentiality of deep learning techniques applied to wireless communications. In this context, the autoencoder is represented by a deep neural network which takes as input a sequence of bits \(\mathbf {s}\) , produces a coded signal \(\mathbf {x}\)  fed into a channel layer with conditional probability density function \(p_{Y}(\mathbf {y}|\mathbf {x})\) , and tries to reconstruct the input sequence of bits from the channel output samples \(\mathbf {y}\) . Autoencoders are usually trained via cross-entropy minimization in order to reduce the block-error rate (BLER) of the full system. Only recently however, different metrics, such as bit-error rate (BER) metrics [5]} or the information rate [6]}, have been considered in the problem formulation. In particular, since the channel capacity constitutes the upper bound on the information rate, the maximization of the mutual information between the channel input and output has been discussed in [7]}, [8]}.
